Course Topics:
 

• Math Foundations for data science (we will start from first principles)

      Linear Algebra, Probability, Data/Signal Processing, Machine learning and Convex optimization --> Data Science

       We will introduce and revisit these concepts through the various applications discussed in the course.
 

• GPS, WiFi, Localization, Sensor Fusion

      Algorithms: Triangulation, Trilateration, Clustering, SLAM, Kalman filter

       Applications: Outdoor and indoor localization, mapping, IoT
 

• IMU Sensors, Motion Tracking

      Algorithms: Dead reckoning, PCA, Hidden Markov Models (HMM), Kalman Filters

      Applications: Activity tracking, Gesture recognition, Sports analytics, wearable computing
 

• Microphone, Speakers

      Algorithms: SVD, Noise Cancellation, Dynamic Time Warping, Gradient Descent

      Applications: Acoustic Sensing, Voice Assistants, Earphone computing, AR/VR
 

• Camera, Light

      Algorithms: SIFT/SURF, Wavelets, 3D Point clouds, Structure from motion (SfM)

      Applications: Augmented reality, Visual Communication, Shadow sensing
 

• Wireless Radios (WiFi, BLE, 5G)

      Algorithms: Beamforming, Time of Flight (ToF), Clock synchronization, FMCW, Doppler

      Applications: Presence detection, Liquid identification, Bio-monitoring, Digital agriculture

 

• Security and Privacy

      Algorithms: Classification, Non-linearity, Viterbi decoding, MLE, Stochastic gradient descent

      Applications: Fingerprinting, side channel, inference
 

• Emerging areas: Edge computing, battery-free devices, Earable computing, autonomous cars, …

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   Grading Information:
    - Homework, Assignments(2-3):   20%
    - Paper reviews (5-8):                    10%
    - Machine problems (5-6):             40%  (best of N-1 MPs)
    - 1 mid-term exam (Apr 21st):      30%
    - 4 credit students:                         Will need to do all N MPs